Machine Learning - Practice Test

[#261] What is the approach of basic algorithm for decision tree induction?

(A) greedy

(B) top down

(C) procedural

(D) step by step

(E) greedy

(F) top down

(G) procedural

(H) step by step

Correct Answer

(A) greedy
(E) greedy

[#262] Can we extract knowledge without apply feature selection

(A) Yes

(B) No

(C) Yes

(D) No

Correct Answer

(A) Yes
(C) Yes

[#263] Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

(A) 0.05

(B) 0.06

(C) 0.07

(D) 0.09

(E) 0.05

(F) 0.06

(G) 0.07

(H) 0.09

Correct Answer

(B) 0.06
(F) 0.06

[#264] When the number of classes is large Gini index is not a good choice.

(A) TRUE

(B) FALSE

(C) TRUE

(D) FALSE

Correct Answer

(A) TRUE
(C) TRUE

[#265] Data used to build a data mining model.

(A) training data

(B) validation data

(C) test data

(D) hidden data

(E) training data

(F) validation data

(G) test data

(H) hidden data

Correct Answer

(A) training data
(E) training data

Machine Learning - Study Mode

[#261] What is the approach of basic algorithm for decision tree induction?

Correct Answer

[#262] Can we extract knowledge without apply feature selection

Correct Answer

[#263] Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

Correct Answer

[#264] When the number of classes is large Gini index is not a good choice.

Correct Answer

[#265] Data used to build a data mining model.

Correct Answer